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This study was conducted to examine the effect of compensation on employee performance through a literature review approach. The background of this research is based on the importance of compensation as a factor that contributes to improving employee work outcomes. Compensation is not only considered as remuneration for work performed, but also as an element that can encourage motivation and productivity. The study applied a descriptive method using a literature review approach. The data used were secondary data obtained from 20 national scientific journals published since 2020, discussing compensation and employee performance. Data collection was carried out through library research by selecting, classifying, and reviewing relevant literature sources. Furthermore, the data were analyzed descriptively by interpreting findings from previous studies. The results indicate that 19 out of 20 journals reported a positive and significant relationship between compensation and employee performance. Therefore, it can be concluded that compensation plays an essential role in enhancing employee performance, making an effective compensation system a crucial aspect of human resource management.

This study is motivated by the importance of economic growth as a key indicator of regional development performance and public welfare improvement. Economic growth reflects the ability of local governments to manage resources and implement effective fiscal policies. This research aims to analyze the development and the influence of Local Own-Source Revenue, General Allocation Fund, and Special Allocation Fund on the economic growth of regencies/cities in Jambi Province during the period 2020–2024. The study employs a quantitative approach combined with descriptive analysis. Secondary data are obtained from official publications of relevant institutions and analyzed using panel data regression with the Common Effect Model approach. The results indicate that Local Own-Source Revenue and the General Allocation Fund have a significant effect on economic growth, showing that the increase in regional revenue and fiscal transfers contributes to economic performance. Meanwhile, the Special Allocation Fund does not have a significant effect, indicating that its allocation may not be optimally utilized in stimulating regional economic activities. These findings imply that strengthening regional fiscal capacity and improving the effectiveness of fund allocation are essential to promote sustainable economic growth. The study also highlights the need for better policy coordination and efficient financial management at the regional level to reduce disparities and enhance development outcomes.  

This study aims to analyze the influence of incentives and physical work environment on employee performance with motivation as a mediating variable at the Denpasar City Cultural Office. This study uses a quantitative approach with a survey method. The research population was all employees of the Denpasar Cultural Office totaling 54 people, all of whom were sampled using saturated sampling techniques. Data were collected through a questionnaire with a Likert scale and analyzed using Structural Equation Modeling based on Partial Least Square (SEM-PLS). The results of the study showed that incentives had a positive and significant effect on employee performance, while the physical work environment did not have a significant effect on employee performance. In addition, motivation has a positive and significant effect on employee performance. Mediation testing showed that motivation was able to mediate the influence of incentives on employee performance, but was unable to mediate the influence of the physical work environment on employee performance. These findings indicate that improving employee performance is more effective through providing the right incentives and increasing work motivation compared to improving the physical work environment alone.

This study was conducted at PT. Asmin Bara Bronang, Sepan Uring Village, Kapuas Tengah District, Kapuas Regency, Central Kalimantan Province, with the aim of analyzing the productivity and influencing factors of the PC 300 excavator in coal getting activities to support the achievement of production targets. The objective of this research is to analyze the actual productivity and the factors affecting it in Sector 7 coal getting operations. The research method used field observation with a quantitative descriptive analysis approach. The results show that the productivity of the PC 300 excavator ranges from 127.12 to 224.29 tons/hour, with an average of 173.98 tons/hour. In several conditions, the productivity is still below the company’s target of 180 tons/hour. The analysis indicates that productivity is influenced by material conditions, particularly the Hardgrove Grindability Index (HGI) value of 47, which reflects relatively harder material with coarser particle size, and a moisture content of 22.71%, causing the material to be sticky and cohesive, thereby affecting the bucket filling process. In addition, operational factors such as bottom loading patterns, limited number of tailgate dump trucks, and suboptimal selection of dump truck types also contribute to productivity performance.  Based on the findings, it can be concluded that the productivity of the PC 300 excavator has not consistently met the company’s target. Therefore, improvement efforts are required, including the implementation of top loading methods, increasing the number of tailgate dump trucks, optimizing the selection of hauling equipment, and controlling material conditions that affect the digging process.    

Self-Compacting Concrete (SCC) requires a high cement content, which contributes to increased carbon emissions; therefore, this study evaluates the effect of partial cement substitution with fly ash (5%, 10%, and 15%) and the addition of Polyethylene Terephthalate (PET) waste (0.5% and 0.7%) on the mechanical properties of SCC with a target strength of f’c 30 MPa. The research employed laboratory experimental methods, including fresh concrete tests (slump flow, L-box, and V-funnel) and hardened concrete tests (compressive, tensile, and flexural strength) at 7 and 28 days. The results indicate that fly ash substitution enhances compressive strength, with the highest value of 49.59 MPa achieved at 5% fly ash at 28 days, exceeding normal concrete (34.73 MPa). The addition of PET tends to reduce compressive strength due to increased porosity; however, it significantly improves flexural strength, as the combination of 5% fly ash and 0.5% PET achieved 4.7 MPa compared to 2.9 MPa for normal concrete. Overall, the combination of fly ash and PET waste shows potential for application in structural elements requiring high flexural performance.

This study aims to analyze the influence of workforce agility, job satisfaction, and job engagement on employee adaptive performance at PT. MMT Buana Logistik. The dynamic logistics industry requires employees who have high adaptability to technological changes and operational demands. This type of research is quantitative causality with an explanatory approach. The population in this study were all employees of PT. MMT Buana Logistik, totaling 49 people, with the sampling technique used saturated sampling (census). Data were collected through questionnaires with a 5-point Likert scale. Data analysis techniques used included instrument testing (validity and reliability), testing of classical assumptions (normality, multicollinearity, and heteroscedasticity), and multiple linear regression analysis. Hypothesis testing was carried out through t-tests (partial), F-tests (simultaneous) and coefficient of determination (R2). The results showed that workforce agility, job satisfaction, and job engagement partially and simultaneously have a positive and significant influence on employee adaptive performance. These findings imply the importance of managing work flexibility, fulfilling employee satisfaction, and increasing work engagement to build adaptive human resources to support company competitiveness in the logistics industry.  

This study aims to analyze the aesthetic values of Ranggawis Dance as a form of regional creative dance that develops in Purbalingga Regency. This research employs a qualitative method with a descriptive approach, supported by a choreographic aesthetic approach. The theoretical framework used is the aesthetic theory proposed, which includes aspects of form, content, and performance. Data collection techniques were carried out through observation, interviews, and documentation involving choreographers, musicians, dancers, and related parties in the performance of Ranggawis Dance. The data were analyzed descriptively using a qualitative approach by linking field findings with the applied aesthetic concepts. The results show that the aesthetics of Ranggawis Dance are formed through the harmony of form aspects, including movement, musical elements, and dancer composition; content aspects reflecting values of togetherness and joy within the community; and performance aspects demonstrated through synchronization (rampak) and dancers’ expressions. The dance movements, which combine Banyumasan, Jaipong, and contemporary elements, create a dynamic and harmonious impression, while the use of pentatonic scales strengthens the traditional character. The collaborative creative process between choreographers and musicians also contributes to the overall aesthetic unity of the performance. Therefore, Ranggawis Dance possesses strong aesthetic values, reflects local cultural identity, and has the potential to be further developed as part of traditional art preservation.

This study was conducted to determine the best log-linear model describing the relationship between gender, sleep duration, extracurricular activities, and student learning outcomes at SMAN 1 Kualuh Hulu. A log-linear analysis approach was used to identify interaction patterns among the variables and evaluate their contribution to student academic performance. The findings showed that the selected model adequately explained the structure of the observed data. A significant three-way interaction was identified between learning outcomes, sleep duration, and gender. The results indicated that insufficient sleep duration had a negative impact on student learning outcomes, and this effect varied by gender. Female students were found to be more vulnerable to decreased academic performance caused by sleep disturbances compared to male students. Meanwhile, extracurricular activities did not demonstrate a significant direct interaction with learning outcomes. This suggests that participation in extracurricular activities was not a major factor influencing differences in academic achievement among students at SMAN 1 Kualuh Hulu.

The implementation of predictive maintenance supported by SAP Plant Maintenance (SAP PM) at PT Xyz has proven to be effective in reducing machine downtime, lowering maintenance costs, and improving asset reliability. The integration of SAP PM with Industry 4.0 technologies such as IoT sensors, AI-based analytics, and real-time notification systems strengthens operational efficiency and ensures continuous performance. Empirical results show improvements in key performance indicators, including a 20-25% reduction in downtime, a 30% reduction in maintenance costs, an increase in asset availability to 97%, an MTBF extension of up to 511 hours, and an OEE rate of 92.1%. These findings highlight the strategic role of digital predictive maintenance in increasing competitiveness and supporting long-term sustainability in manufacturing operations.

Heating, Ventilating, and Air Conditioning (HVAC) systems often suffer from significant energy wastage due to their inability to adapt to real-time environmental changes, leading to high operational costs. Although Proportional-Integral-Derivative (PID) controllers are widely used for their simplicity and reliability, they struggle to handle the complex dynamics of modern environments, requiring advanced optimization to enhance efficiency. This study aims to optimize PID controllers by integrating the Queen Honey Bee Migration (QHBM) algorithm to improve HVAC performance, energy efficiency, and adaptability. The research method employs an experimental approach that compares the performance of conventional PID controllers with PID controllers optimized using the QHBM algorithm under dynamic environmental conditions. The results show that the PID-QHBM system significantly outperforms the conventional PID system, achieving a rise time of 0.2649 seconds and a settling time of 1.6874 seconds with an almost negligible steady-state error of 9.4991e-08. Although it experiences a slight overshoot of 16.3810%, the system stabilizes quickly and maintains the target temperature efficiently. In contrast, the conventional PID controller exhibits slower response characteristics, with a rise time of 1.3730 seconds, a settling time of 2.5144 seconds, and a larger steady-state error of 0.0361. This study demonstrates that integrating the QHBM algorithm into PID controllers provides a more effective solution for real-time temperature control, offering substantial improvements in energy efficiency and system performance. The findings contribute to advancing intelligent HVAC control systems that can better adapt to environmental variations while minimizing operational costs.

The Indonesian capital market has become a vital pillar of the national economy, providing opportunities for companies to obtain funding while simultaneously providing an investment vehicle for the wider community. In this context, stocks are the most sought-after instrument due to the potential returns they offer. However, stock investment is constantly faced with uncertainty, with fluctuating stock prices often presenting challenges for investors, especially those without a thorough understanding of the company's fundamental performance. An interesting phenomenon, the starting point of this research, is the quite extreme price movements of BIPI shares over the past decade. From 2015 to 2021, BIPI's share price remained stagnant at Rp 50 per share, a condition often referred to by market participants as "gocap" (goat capit). This condition reflects low investor interest in the company's shares, possibly due to high risk perceptions or unconvincing fundamental performance.

Psychological well-being in the workplace and educational environment is influenced not only by policies and facilities but also by psychological approaches that touch on individuals' mental, emotional, and spiritual aspects. This study aims to explain how psychological approaches can enhance well-being and motivation. The results of the study show that integrating Islamic educational values, mindfulness, and emotional intelligence can help individuals manage stress, increase self-confidence, and strengthen the meaning of life. In addition, adjusting learning materials to the needs of students also plays a role in supporting mental health. Individual motivation can be increased through approaches that align with psychological needs, such as fulfilling basic needs, providing rewards, and supporting autonomy and social relationships. The implementation of strategies such as open communication, good leadership, and a comfortable work environment also contributes to increasing motivation and performance. Thus, the psychological approach has an important role in creating a healthy, productive, and sustainable educational and organizational environment.

Work motivation, job satisfaction, and employee performance are key aspects of management psychology that continue to evolve as organizational productivity demands increase. In an era of global competition, organizations are required to manage human resources effectively by enhancing work motivation and job satisfaction to achieve optimal performance. This study aims to examine the relationship between work motivation and employee performance, as well as to analyze the role of job satisfaction as a mediating variable in that relationship. The method used is a literature review with a qualitative approach. Data were collected through the Google Scholar and Publish or Perish databases using keywords related to work motivation, job satisfaction, and employee performance from a management psychology perspective. From 25 articles found between 2022 and 2026, 5 of the most relevant articles were selected for further analysis.The results of the study indicate that work motivation has a significant influence on employee performance, both directly and indirectly through job satisfaction as a mediating variable. Additionally, there is a growing trend toward the integration of management psychology approaches in efforts to enhance employee performance. Thus, it can be concluded that the simultaneous management of motivation and job satisfaction is a key factor in achieving optimal performance. Further research is recommended to develop a more comprehensive and integrative model.

Digital transformation in the public sector encourages civil servants to utilize technology not only for administrative purposes but also to improve efficiency, accuracy, and work performance. This study examines the influence of Intelligent Learning-Based Training and Digital Training Components on the Digital Productivity of civil servants at the Bureau of Economic Affairs of the Regional Secretariat of Lampung Province, with Adaptive Competence serving as a mediating variable. A quantitative approach with a non-experimental survey design was applied, and data were collected through structured questionnaires distributed to employees engaged in digital-based work activities. The data were analyzed using structural model testing procedures. The findings reveal that Intelligent Learning-Based Training does not significantly influence Adaptive Competence or Digital Productivity. Conversely, Digital Training Components positively and significantly affect both Adaptive Competence and Digital Productivity. Adaptive Competence also has a positive impact on Digital Productivity and mediates the relationship between Digital Training Components and Digital Productivity. However, Adaptive Competence does not mediate the relationship between Intelligent Learning-Based Training and Digital Productivity. These results indicate that civil servants’ digital productivity is more effectively improved through practical, relevant, accessible, and well-evaluated digital training programs than through intelligent learning systems alone. Therefore, public institutions are encouraged to develop digital training initiatives that align with employees’ daily work demands while strengthening their adaptive competence in responding to technological changes.

Waste management remains a critical environmental issue due to the lack of public awareness in separating organic and inorganic waste, resulting in accumulation and environmental pollution This study aims to analyze and evaluate the development of automatic waste sorting systems based on proximity sensors with full-capacity notification using a Systematic Literature Review (SLR) approach.. The proposed system utilizes a combination of sensors, including proximity sensors for material identification and ultrasonic sensors for detecting object presence and bin capacity, integrated with a microcontroller for real-time processing. Additionally, the system is equipped with IoT-based monitoring that allows users to receive notifications when the waste bin reaches its capacity. The research method involves system design, hardware and software integration, and functional testing to evaluate system performance. The results indicate that the system is capable of sorting waste automatically with a high level of accuracy and responsiveness, while also providing real-time monitoring to support waste management operations. The implementation of this system can reduce manual intervention, increase operational efficiency, and promote better waste segregation practices. Furthermore, this study highlights the potential of integrating smart technology into environmental management systems, contributing both theoretically and practically to the development of sustainable waste management solutions.

This study aims to evaluate the determinants of organizational sustainability within the Doctor Peduli Foundation (doctorSHARE), specifically regarding its Floating Hospital program. Specifically, it examines the influence of stakeholder support and operational efficiency on organizational sustainability, while analyzing the role of organizational innovation as a mediating variable in ensuring the continuity of medical services in remote areas. Employing a quantitative approach with a survey method, data were collected from 81 respondents comprising staff and volunteers directly involved in the Floating Hospital's operations. Structural Equation Modeling (SEM) was utilized to analyze the data and test the proposed hypotheses.The results reveal that stakeholder support does not have a significant direct impact on either organizational sustainability or innovation performance. Conversely, operational efficiency emerged as a dominant factor with a significant positive influence. Furthermore, organizational innovation was found to play a crucial role as a mediator, strengthening the impact of operational efficiency on organizational sustainability.This study concludes that the sustainability of non-profit organizations such as doctorSHARE is determined more by management's ability to optimize internal resources efficiently and innovatively than by reliance on external support. Consequently, strengthening internal operational systems should be a primary strategic priority to ensure long-term organizational existence.

The use of moldboard plows in agricultural land preparation often faces wear problems on the share component, leading to increased maintenance time and reduced operational efficiency. This study aims to design and analyze a modular concept for the plow share to improve maintenance and repair efficiency without compromising structural performance. The research methods include field observation, component dimension measurement, design modeling using CAD software, and structural analysis using the Finite Element Analysis  (FEA) method. In addition, maintenance ease was evaluated through a questionnaire based on a rating scale. The results show that the modular design significantly improves maintenance and repair efficiency, with an average score of 4.59 categorized as very good. Structural analysis indicates that the modular design reduces maximum stress on the moldboard and slightly decreases deformation, reflecting improved structural stiffness compared to the conventional design. However, the Safety Factor on the share component remains below the acceptable limit, indicating the need for further development. Overall, the modular design provides an effective solution to enhance maintenance efficiency while maintaining the structural performance of the moldboard plow.

The shaft is a crucial component in mechanical systems because it serves to transfer power and rotational motion throughout the machine. This research aims to assess the structural strength and operational performance of shafts used in a tire shredding machine through numerical simulation methods in order to achieve a safe and efficient design. The study involved several stages, including the development of shaft geometry models, the determination of boundary conditions, load application, mesh generation, and stress analysis using the finite element method. Two shaft configurations were examined: a 59 mm diameter shaft made from AISI 1045 steel and a 49 mm diameter shaft manufactured from ASTM A36 steel. The simulation results indicate that the 59 mm shaft experiences a Von Mises stress of 8.9 × 10⁻⁵ MPa, with a maximum displacement of 0 mm and a safety factor of 15. Similarly, the 49 mm shaft shows a Von Mises stress of 8.4 × 10⁻⁵ MPa, no measurable displacement, and a safety factor of 15. These findings confirm that both shaft designs are capable of safely withstanding the applied working loads. In addition, cutting system tests revealed that a 24-tooth blade achieved an efficiency of 26.9%, while a 40-tooth blade reached only 22.3%, indicating that the 24-tooth configuration provides better performance.

The implementation of Hospital Management Information Systems (HMIS) has become a strategic imperative to enhance operational efficiency amidst the ongoing global digital health transformation era. This study aims to analyze the determinants of successful HMIS implementation and its impact on service efficiency and patient outcome quality. Employing a narrative review approach with thematic synthesis of literature from PubMed, Scopus, and ScienceDirect databases between 2016-2026, the study evaluates the relationship between technology investment and organizational performance. The synthesis results indicate that while HMIS significantly reduces administrative burdens and medication errors, a "digital paradox" exists where technical efficiency may disrupt interpersonal interactions between healthcare providers and patients if systems are not user-centered. Key success factors include data interoperability (HL7/FHIR standards), human resource readiness, and governance policy support. This study concludes that a patient-centered efficiency model is the fundamental basis for ensuring technology investments yield sustainable added value within the healthcare delivery system.

Predictive maintenance (PdM) plays a crucial role in modern industrial systems by minimizing downtime, reducing maintenance costs, and optimizing asset performance. However, many predictive models operate as “black box” systems, limiting transparency and making it difficult for operators to interpret their outputs. This study aims to integrate Explainable Artificial Intelligence (XAI) techniques with Remaining Useful Life (RUL) prediction models to improve both accuracy and interpretability. Various machine learning and deep learning approaches, including Support Vector Machines (SVM), Random Forest (RF), XGBoost, Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN), are employed to predict RUL using real-time sensor data from rotating machinery. XAI methods such as SHAP, LIME, and attention mechanisms are applied to provide human-understandable explanations of model predictions. The models are evaluated based on accuracy, Root Mean Square Error (RMSE), and interpretability scores. The results show that XAI-enhanced models outperform traditional approaches in predictive performance while offering greater transparency. These explanations help maintenance engineers better understand the factors influencing predictions, thereby improving decision-making and trust in the system. Nevertheless, the integration of XAI introduces additional computational complexity, which may pose challenges for large-scale industrial implementation. Overall, this study highlights the potential of combining XAI with RUL prediction to develop more reliable, transparent, and effective predictive maintenance solutions.